Fix timestep dtype in legacy inpaint (#2120)

* Fix timestep dtype in legacy inpaint

This matches the structure in the text2img, img2img, and inpaint ONNX pipelines

* Fix style in dtype patch

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py

@@ -10,7 +10,7 @@ from transformers import CLIPFeatureExtractor, CLIPTokenizer
 from ...configuration_utils import FrozenDict
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from ...utils import deprecate, logging
-from ..onnx_utils import OnnxRuntimeModel
+from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
 from ..pipeline_utils import DiffusionPipeline
 from . import StableDiffusionPipelineOutput
 
@@ -391,6 +391,10 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
 
         t_start = max(num_inference_steps - init_timestep + offset, 0)
         timesteps = self.scheduler.timesteps[t_start:].numpy()
+        timestep_dtype = next(
+            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
+        )
+        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
 
         for i, t in enumerate(self.progress_bar(timesteps)):
             # expand the latents if we are doing classifier free guidance
@@ -398,9 +402,10 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
             latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
             # predict the noise residual
-            noise_pred = self.unet(
-                sample=latent_model_input, timestep=np.array([t]), encoder_hidden_states=prompt_embeds
-            )[0]
+            timestep = np.array([t], dtype=timestep_dtype)
+            noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[
+                0
+            ]
 
             # perform guidance
             if do_classifier_free_guidance: